Automotive to Analytics.

A Mini-Guide to Career Change.

I’ve been thinking a lot about career change lately. No, not because I’m ready to undergo a career change (heck I still consider myself coming out of career change), but more so because I just passed the six year mark of when I decided to take the leap myself. I enjoy spending time reflecting on what some days feels like an eternity ago while others I feel I’m still trying to play catch up. During this transition I’ve taken notes on habits, the good, bad and the unhealthy along the way. Useful or not, I thought I’d take a stab at sharing them (as well as take a stab at writing my first Medium post!). Before we dive in, a quick background so we know where I was jumping from and what I jumped into.

Where I was coming from:

Left for another time, let’s just say I was not coming out of high school academically prepared for a D1 school. I did landscaping for a year and did some odd jobs here and there. I decided to give school another shot. Enrolled in community college and graduated with an Associates of Applied Science in Automotive Technology. Got a job in a Honda Dealership and within a short time, became a fully Certified Honda Master Tech. ~8months later, after a little over 4 years of working really hard to learn all I could about diagnosing and fixing cars, I jumped into the abyss that is career change.

Where did I go?

Analytics... I know, it seems a far cry from automotive but you’d be surprised (or not) how similar diagnosing a misfire can be to diagnosing bugs in my python code. I learned some skills while in automotive that became quite useful in business and analytics, how to be an effective problem solver and how to communicate technical problems to non-technical people.

Ok! So how did we get here, from tuning engines to hyper-parameters, rethreading bolts to debugging SQL? I’m sure this is over simplifying the entire process but I followed this blueprint.

1. Be specific and realistic about your short term goals, set bigger and ambitious mid to long term goals.

Here is an example. I had decided I needed to leave automotive. After a couple months research I realized Information Technology with a focus in Data Engineering was going to suit me well. I also had a young family so I couldn’t abandon ship and just go back to school.

• Short Term (the next 6–12 months): Find a transitional job that would cover the bills, bring me closer to a professional setting (and around like minded people), and maybe (very small maybe) grow into a tech job.
• Longer term (the next 18+ months): I’d be a year and a half into my return to school and depending on coursework completed, possibly find my way into some sort of data type work. At this time my direction was Data Engineer.

What I found useful here was to assess the longer term goal as my short term milestones were met. This allowed me to correct course as necessary, change direction, or set new long term goals.

2. Pick a learning path. Pivot as needed.

From what level and direction do you want to approach this learning? Let’s look at the umbrella that is Data Science. Are you going to come at it learning Stats first and then programming, and then gain intuition about Linear Algebra and Calculus (this is assuming little to no knowledge or it’s been quite some time since you tested a Hypothesis). Will you learn database architecture and grow your SQL skills, then automation, data visualization, and descriptive stats, then do the machine learning if you’re interested? Keep in mind these are not all mutually inclusive or exclusive for any one role, just an example.

Set a long term goal of what you think you want to do (Data Engineer, Software Engineer, Web Design, etc.) and start learning. If you find along the way that there is one area that really just resonates with you, attack that for a while (pivot)! For me, I found I wanted to still do the automation work but that I also loved this idea of creativity through data viz, machine learning, and being close to the business. So I changed my major shortly after starting school to Data Analytics degree and started focusing my efforts on landing a job in Analytics.

Strategically time-box your day.

This one took time and frankly, building and breaking a bunch of bad habits. A single habit during your day might be the time you wake up. Another might be a time you work out. But now, assuming you’re working and making a career shift simultaneously which may also involve a return to higher education, creating repeatable daily habits can help nudge you’re success forward. I would almost say these are routines but I felt that was too rigid sounding. I like habits because they sound to me like little time boxes throughout my day where I can do things and then in between those time boxes the daily chaos is allowed to ebb and flow. Find times that work for you. Protect those times and stick to doing the task. If its morning study and nighttime testing, do that. If its project based learning and you do best in the afternoon, find a way to fit that into your schedule. Either way, find what works through trial and error, try and recognize if a habit is becoming unhealthy and either limit or completely stop that habit. For example, as I moved into more advanced analytics and some machine learning, I was simply fascinated by the process that I would sometimes stay up until 2 in the morning just hacking away (mind you my morning habit is to wake up by 4:45)! Needless to say, that had to stop.

Persist, persist, and persist once more!!

This is the hardest one. No matter how much we try and not fall into the “work harder” mentality, there will undoubtedly be an element of this. Some days will be exciting, others will be victorious, and then there will be the many days of banging you’re head on a wall, self-doubt, and good ole Imposter Syndrome. For me, I found comfort in trying to connect with the Analytics community as well as listening to my podcasts and learning that almost everyone goes through, and honestly still deals with the days like that. But persisting is key. There are two quotes I really like about success and failure.

The first is Thomas Edison’s famous, “I have not failed. I’ve just found 10,000 ways that won’t work.” Remember, that struggle feeling is learning and as one of the best leader’s I’ve had the pleasure of working for used to say, “There is no failure, we win or we learn.”

The other quote is by Winston Churchill, “Success is not final, failure is not fatal: it is the courage to continue that counts.” Failure can sting, but it can also be a great educator. I think I learned the most from my failures (which are many I might add!).

So whether you are contemplating a complete 180 career change or are in the midst of one (especially jumping into Analytics) you’ve totally got this. Yes it can be challenging, frustrating, tiring, and all the other negative “ings” but if you asked me knowing now what I know about making the change would I have done it differently? Absolutely not (well maybe just not pulling the all-nighters so frequently). And I’d definitely do it again. The more I learn, the more I realize there is to learn so, if you’re someone who lives to learn, then this train is definitely the one you’ve been waiting for.